Kernel Estimation of Truncated Volterra Filter Model Based on DFP Technique and Its Application to Chaotic Time Series Prediction

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英文篇名：Kernel Estimation of Truncated Volterra Filter Model Based on DFP Technique and Its Application to Chaotic Time Series Prediction 作者：ZHANG ; Yumei ; BAI ; Shulin ; LU ; Gang ; WU ; Xiaojun 英文作者：ZHANG Yumei;BAI Shulin;LU Gang;WU Xiaojun;Key Laboratory of Modern Teaching Technology,Ministry of Education,Shaanxi Normal University;School of Computer Science,Shaanxi Normal University;School of Electronics and Information,Northwestern Polytechnical University; 英文关键词：Chaos;;Davidon-Fletcher-Powell algorithm;;Prediction model;;Second-order Volterra filter;;Speech signal 中文刊名：EDZX 英文刊名：电子学报(英文) 机构：Key Laboratory of Modern Teaching Technology,Ministry of Education,Shaanxi Normal University;School of Computer Science,Shaanxi Normal University;School of Electronics and Information,Northwestern Polytechnical University; 出版日期：2019-01-15 出版单位：Chinese Journal of Electronics 年：2019 期：v.28 基金：supported by the National Natural Science Foundation of China(No.11502133,No.11772178,No.11372167);; the National Key Research and Development Program of China(No.2017YFB1402102);; the 111 project(No.B18032);; the Fundamental Research Funds for the Central Universities(No.GK201703082,No.GK201801004) 语种：英文; 页：EDZX201901018 页数：9 CN：01 ISSN：10-1284/TN 分类号：131-139

摘要

In order to overcome some problems caused by improper parameters selection when applying Least mean square(LMS), Normalized LMS(NLMS) or Recursive least square(RLS) algorithms to estimate coefficients of second-order Volterra filter, a novel DavidonFletcher-Powell-based Second-order Volterra filter(DFPSOVF) is proposed. Analysis of computational complexity and stability are presented. Simulation results of system parameter identification show that the DFP algorithm has fast convergence and excellent robustness than LMS and RLS algorithm. Prediction results of applying DFPSOVF model to single step predictions for Lorenz chaotic time series illustrate stability and convergence and there have not divergence problems. For the measured multiframe speech signals, prediction accuracy using DFPSOVF model is better than that of Linear prediction(LP).The DFP-SOVF model can better predict chaotic time series and the real measured speech signal series.
        In order to overcome some problems caused by improper parameters selection when applying Least mean square(LMS), Normalized LMS(NLMS) or Recursive least square(RLS) algorithms to estimate coefficients of second-order Volterra filter, a novel DavidonFletcher-Powell-based Second-order Volterra filter(DFPSOVF) is proposed. Analysis of computational complexity and stability are presented. Simulation results of system parameter identification show that the DFP algorithm has fast convergence and excellent robustness than LMS and RLS algorithm. Prediction results of applying DFPSOVF model to single step predictions for Lorenz chaotic time series illustrate stability and convergence and there have not divergence problems. For the measured multiframe speech signals, prediction accuracy using DFPSOVF model is better than that of Linear prediction(LP).The DFP-SOVF model can better predict chaotic time series and the real measured speech signal series.

引文

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